Analysis of Censored Data Using Dirichlet Process Mixture Model with Generalized Inverse Weibull Distribution as Kernel

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Haji Joudaki B.; Hashemi R.; Khazaei S.

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Journal Paper

Paper Information

Journal: JOURNAL OF STATISTICAL SCIENCES Year:2023 | Volume:17 | Issue:2 Page(s): 275-298

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Information Journal Paper

Title

Analysis of Censored Data Using Dirichlet Process Mixture Model with Generalized Inverse Weibull Distribution as Kernel

Author(s)

Haji Joudaki B. | Hashemi R. | Khazaei S. | Issue Writer Certificate

Keywords

Mixture model

Dirichlet process

Generalized inverse Weibull distribution

Right censoring

Markov chain monte carlo.

Abstract

In this paper, a new Dirichlet process mixture model with the generalized inverse Weibull distribution as the kernel is proposed. After determining the prior distribution of the parameters in the proposed model, Markov Chain Monte Carlo methods were applied to generate a sample from the posterior distribution of the parameters. The performance of the presented model is illustrated by analyzing real and simulated data sets, in which some data are right-censored. Another potential of the proposed model demonstrated for data clustering. Obtained results indicate the acceptable performance of the introduced model.

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